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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: mental/mental-bert-base-uncased
metrics:
  - accuracy
widget:
  - text: How to write a science fiction novel
  - text: Overcoming social anxiety and fear of public speaking
  - text: Supporting a family member with depression
  - text: Understanding stock market trends
  - text: Recipes for homemade Italian pasta
pipeline_tag: text-classification
inference: true
model-index:
  - name: SetFit with mental/mental-bert-base-uncased
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 1
            name: Accuracy

SetFit with mental/mental-bert-base-uncased

This is a SetFit model that can be used for Text Classification. This SetFit model uses mental/mental-bert-base-uncased as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
True
  • 'Exploring historical landmarks in Europe'
  • 'How to create an effective resume'
  • 'Exercises to improve core strength'
False
  • 'Feeling sad or empty for long periods without any specific reason'
  • 'Dealing with the emotional impact of chronic illness'
  • 'Understanding and coping with panic attacks'

Evaluation

Metrics

Label Accuracy
all 1.0

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("richie-ghost/setfit-MedBert-MentalHealth-Topic-Check")
# Run inference
preds = model("Understanding stock market trends")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 4 6.4583 11
Label Training Sample Count
True 22
False 26

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (4, 4)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0132 1 0.2561 -
0.6579 50 0.0078 -
1.0 76 - 0.0067
1.3158 100 0.0012 -
1.9737 150 0.0011 -
2.0 152 - 0.0044
2.6316 200 0.0009 -
3.0 228 - 0.0029
3.2895 250 0.0005 -
3.9474 300 0.0008 -
4.0 304 - 0.0028
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.7.0
  • Transformers: 4.40.0
  • PyTorch: 2.2.1+cu121
  • Datasets: 2.19.0
  • Tokenizers: 0.19.1

Citation

BibTeX

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}